Predictive weighting of hypotension profiling parameters

ABSTRACT

A system having a processor obtain a digital hemodynamic data from a hemodynamic sensor, obtain one or more vital sign parameters characterizing vital sign data from the digital hemodynamic data, derive differential parameters based on the one or more vital sign parameters, generate combinatorial parameters using the one or more vital sign parameters and the differential parameters, determine a risk score corresponding to a probability of a future hypotension event for the living subject based on a weighted combination of a plurality of hypotension profiling parameters including the one or more vital sign parameters characterizing vital sign data, the differential parameters and the combinatorial parameters, and invoke a sensory alarm if the risk score satisfies a predetermined risk criterion.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/634,918, filed Jun. 27, 2017, which claims priority to U.S.Provisional Application No. 62/360,088, filed Jul. 8, 2016, the entirecontents of which are herein incorporated by reference in theirentireties.

BACKGROUND

Hypotension, or low blood pressure, can be a harbinger of seriousmedical complications, and even mortality, for patients undergoingsurgery and those acutely or critically ill patients receiving treatmentin an intensive care unit (ICU). The dangers associated with theoccurrence of hypotension in a patient are due both to the potentialinjury caused by the hypotension itself and to the many seriousunderlying medical disorders that the occurrence of hypotension maysignify.

In and of itself, hypotension in surgical patients or critically illpatients is a serious medical condition. For example, in the operatingroom (OR) setting, hypotension during surgery is associated withincreased mortality and organ injury. Even short durations of extremehypotension during surgery are associated with acute kidney injury andmyocardial injury. Among critically ill patients, in-hospital mortalitymay be nearly doubled for patients experiencing hypotension afteremergency intubation. For surgical patients and seriously ill patientsalike, hypotension, if not corrected, can impair organ perfusion,resulting in irreversible ischemic damage, neurological deficit,cardiomyopathy, and renal impairment.

In addition to posing serious risks to surgical patients and criticallyill patients in its own right, hypotension can be a symptom of one ormore other serious underlying medical conditions. Examples of underlyingconditions for which hypotension may serve as an acute symptom includesepsis, myocardial infarction, cardiac arrhythmia, pulmonary embolism,hemorrhage, dehydration, anaphylaxis, acute reaction to medication,hypovolemia, insufficient cardiac output, and vasodilatory shock. Due toits association with such a variety of serious medical conditions,hypotension is relatively common, and is often seen as one of the firstsigns of patient deterioration in the OR and ICU. For instance,hypotension is seen in up to approximately thirty-three percent ofsurgeries overall, and up to eighty-five percent in high risk surgeries.Among ICU patients, hypotension occurs in from approximately twenty-fourpercent to approximately eighty-five percent of all patients, with theeighty-five percent occurrence being seen among critically ill patients.

Conventional patient monitoring for hypotension in the OR and ICUsettings can include continuous or periodic blood pressure measurement.However, such monitoring, whether continuous or periodic, typicallyprovides no more than a real-time assessment. As a result, hypotensionin a surgical patient or critically ill patient is usually detected onlyafter it begins to occur, so that remedial measures and interventionscannot be initiated until the patient has entered a hypotensive state.Although, as noted above, extreme hypotension can have potentiallydevastating medical consequences quite quickly, even relatively mildlevels of hypotension can herald or precipitate cardiac arrest inpatients with limited cardiac reserve.

In view of the frequency with which hypotension is observed to occur inthe OR and ICU settings, and due to the serious and sometimes immediatemedical consequences that can result when it does occur, a solutionenabling prediction of a future hypotension event, before itsoccurrence, is highly desirable.

SUMMARY

There are provided systems and methods for performing predictiveweighting of hypotension profiling parameters, substantially as shown inand/or described in connection with at least one of the figures, and asset forth more completely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram of an exemplary system for performing predictiveweighting of hypotension profiling parameters, according to oneimplementation;

FIG. 2A shows an exemplary implementation for non-invasively detectingarterial pressure at an extremity of a living subject;

FIG. 2B shows an exemplary implementation for performing minimallyinvasive detection of arterial pressure of a living subject;

FIG. 3 shows an exemplary system and a computer-readable non-transitorymedium including instructions enabling performance of predictiveweighting of hypotension profiling parameters;

FIG. 4 is a flowchart presenting an exemplary method for use by a healthmonitoring system to perform predictive weighting of hypotensionprofiling parameters; and

FIG. 5 shows a trace of an arterial pressure waveform includingexemplary indicia corresponding to the probability of future hypotensionin a living subject.

DETAILED DESCRIPTION

The following description contains specific information pertaining toimplementations in the present disclosure. One skilled in the art willrecognize that the present disclosure may be implemented in a mannerdifferent from that specifically discussed herein. The drawings in thepresent application and their accompanying detailed description aredirected to merely exemplary implementations. Unless noted otherwise,like or corresponding elements among the figures may be indicated bylike or corresponding reference numerals. Moreover, the drawings andillustrations in the present application are generally not to scale, andare not intended to correspond to actual relative dimensions.

The present application discloses systems and methods for performingpredictive weighting of hypotension profiling parameters. By convertingdata received from a hemodynamic sensor to digital hemodynamic data of aliving subject, and by transforming the digital hemodynamic data tomultiple hypotension profiling parameters, the present solution employsa powerful multivariate model for predicting future hypotension.

The solution disclosed by the present application utilizes themultivariate model to determine a risk score corresponding to theprobability of a future hypotension event for the living subject basedon a weighed combination of the multiple hypotension profilingparameters. In addition, by invoking a sensory alarm if the risk scoresatisfies a predetermined risk criterion, the present applicationdiscloses a solution that provides an early warning of a futurehypotension event for the living subject, thereby advantageouslyenabling health care workers to prepare a timely and effectiveintervention.

FIG. 1 shows a diagram of an exemplary system for performing predictiveweighting of hypotension profiling parameters, according to oneimplementation. Health monitoring system 100 includes hardware unit 102,which may be an integrated hardware unit, for example, including systemprocessor 104, implemented as a hardware processor, analog-to-digitalconverter (ADC) 122, and system memory 106. As shown in FIG. 1, healthmonitoring system 100 also includes hypotension prediction software code110 including predictive weighting module 116, stored in system memory106. System memory 106 is further shown to store hypotension profilingparameters 112 including one or more vital sign parameterscharacterizing vital sign data, which will be described in greaterdetail below.

As further shown in FIG. 1, health monitoring system 100 also includeshemodynamic sensor 140 coupled to hardware unit 102, and display 126providing user interface 120. In addition, health monitoring system 100includes digital-to-analog converter 124 (hereinafter “DAC 124”),digital hemodynamic data 144 generated by ADC 122 from signal 142received from hemodynamic sensor 140, and sensory alarm 128.

Health monitoring system 100 may be implemented within a patient careenvironment such as an intensive care unit (ICU) or operating room (OR),for example. As shown in FIG. 1, in addition to health monitoring system100, the patient care environment includes patient 130 (hereinafter“living subject 130”), and healthcare worker 150 (hereinafter “user150”) trained to utilize health monitoring system 100. As will bediscussed in greater detail below, user interface 120 is configured toreceive inputs 152 from user 150, and to provide sensory alarm 128 if arisk score predictive of a future hypotension event for living subject130 satisfies a predetermined risk criterion.

Hemodynamic sensor 140 is shown in an exemplary implementation in FIG.1, and is attached to living subject 130. It is noted that hemodynamicsensor 140 may be a non-invasive or minimally invasive sensor attachedto living subject 130. In one implementation, as represented in FIG. 1,hemodynamic sensor 140 may be attached non-invasively at an extremity ofliving subject 130, such as a wrist or finger of living subject 130.Although not explicitly shown in FIG. 1, in other implementations,hemodynamic sensor 140 may be attached non-invasively at an ankle or toeof living subject 130. Signal 142 received by health monitoring system100 from hemodynamic sensor 140 may include signals corresponding to thearterial pressure of living subject 130, such as an arterial pressurewaveform of living subject 130. Health monitoring system 100 andhemodynamic sensor 140 may be configured such that signal 142 may bereceived by health monitoring system 100 wirelessly, or via a wiredconnection with hemodynamic sensor 140.

According to the exemplary implementation shown in FIG. 1, systemprocessor 104 is configured to utilize ADC 122 to convert signal 142 todigital hemodynamic data 144. System processor 104 is further configuredto execute hypotension prediction software code 110 to transform digitalhemodynamic data 144 to multiple hypotension profiling parameters 112.It is noted that hypotension profiling parameters 112 include one ormore vital sign parameters characterizing vital sign data, as well asdifferential and combinatorial parameters derived from the one or morevital sign parameters.

System processor 104 is further configured to execute hypotensionprediction software code 110 to use predictive weighting module 116 todetermine a risk score corresponding to the probability of a futurehypotension event for living subject 130 based on a weighted combinationof hypotension profiling parameters 112. In addition, system processor104 is configured to execute hypotension prediction software code 110 toinvoke sensory alarm 128 if the risk score satisfies a predeterminedrisk criterion. For example, hypotension prediction software code 110may invoke sensory alarm 128 to warn of a hypotension event for livingsubject 130 predicted to occur approximately one to five minutes in thefuture, or up to approximately thirty minutes in the future.

In various implementations, sensory alarm 128 may be implemented as oneor more of a visual alarm, an audible alarm, and a haptic alarm. Forexample, when implemented to provide a visual alarm, sensory alarm 128may be invoked as flashing and/or colored graphics shown by userinterface 120 on display 126, and/or may include displaying the riskscore via user interface 120 on display 126. When implemented to providean audible alarm, sensory alarm 128 may be invoked as any suitablewarning sound, such as a siren or repeated tone. Moreover, whenimplemented to provide a haptic alarm, sensory alarm 128 may causehardware unit 102 to vibrate or otherwise deliver a physical impulseperceptible to user 150.

It is noted that the risk score of living subject 130 is determinedbased on a weighted combination of hypotension profiling parameters 112,which are derived from signal 142 of living subject 130 received fromhemodynamic sensor 140. Consequently, according to the inventiveconcepts disclosed by the present application, system processor 104 ofhealth monitoring system 100 is configured to execute hypotensionprediction software code 110 to determine the risk score of livingsubject 130 without comparison with data corresponding to hypotension inother living subjects. In other words, hypotension prediction softwarecode 110 determines the risk score of living subject 130 based onquantities derived from the one or more vital sign parameterscharacterizing vital sign data, without reference to a hypotensionpatient database storing information regarding hypotension in patientsother than living subject 130.

In addition to the functionality described above, in someimplementations, system processor 104 may be configured to executehypotension prediction software code 110 to identify a most probablecause of the future hypotension event for living subject 130. Forexample, based on indicia included in digital hemodynamic data 144,hypotension prediction software code 110 may be able to identify poorvascular tone, low blood volume, or reduced cardiac contractility, toname a few exemplary causes, as a most probable cause of a predictedfuture hypotension event. Furthermore, in some implementations, systemprocessor 104 may be configured to execute hypotension predictionsoftware code 110 to recommend a medical intervention for preventing thefuture hypotension event for living subject 130. With respect to thefirst and second example causes of hypotension identified above,administration of a vasoconstrictor may be recommended if poor vasculartone is detected, while administration of saline or whole blood may berecommend if low blood volume is identified as a most probable cause ofthe predicted future hypotension event.

Referring to FIG. 2A, FIG. 2A shows an exemplary implementation forsensing arterial pressure non-invasively at an extremity of a livingsubject. Health monitoring system 200A, in FIG. 2A, includes hardwareunit 202 having ADC 222 and hypotension prediction software code 210including predictive weighting module 216. As shown by FIG. 2A, thearterial pressure of living subject 230 is detected non-invasively atfinger 232 of living subject 230 using hemodynamic sensing cuff 240 a.Also shown in FIG. 2A are signal 242 received by health monitoringsystem 200A from hemodynamic sensing cuff 240 a, digital hemodynamicdata 244 generated by ADC 222 from signal 242, and hypotension profilingparameters 212 including one or more vital sign parameterscharacterizing vital sign data, obtained through transformation ofdigital hemodynamic data 244 by hypotension prediction software code210.

Living subject 230, signal 242, and hemodynamic sensing cuff 240 acorrespond respectively in general to living subject 130, signal 142,and hemodynamic sensor 140, in FIG. 1, and may share any of thecharacteristics attributed to those corresponding features in thepresent application. Moreover, hardware unit 202 having ADC 222 andhypotension prediction software code 210 including predictive weightingmodule 216, in FIG. 2A, corresponds in general to hardware unit 102having ADC 122 and hypotension prediction software code 110 includingpredictive weighting module 116, in FIG. 1, and may share any of thecharacteristics attributed to that corresponding feature in the presentapplication.

According to the implementation shown in FIG. 2A, hemodynamic sensingcuff 240 a is designed to sense an arterial pressure of living subject230 non-invasively at finger 232 of living subject 230. Moreover, asshown in FIG. 2A, hemodynamic sensing cuff 240 a may take the form of asmall, lightweight, and comfortable hemodynamic sensor suitable forextended wear by living subject 230. It is noted that althoughhemodynamic sensing cuff 240 a is shown as a finger cuff, in FIG. 2A, inother implementations, hemodynamic sensing cuff 240 a may be suitablyadapted as a wrist, ankle, or toe cuff for attachment to living subject230.

It is further noted that the advantageous extended wear capabilitydescribed above for hemodynamic sensing cuff 240 a when implemented as afinger cuff may also be attributed to wrist, ankle, and toe cuffimplementations. As a result, hemodynamic sensing cuff 240 a may beconfigured to provide substantially continuous beat-to-beat monitoringof the arterial pressure of living subject 230 over an extended periodof time, such as minutes or hours, for example.

Continuing to FIG. 2B, FIG. 2B shows an exemplary implementation forperforming minimally invasive detection of arterial pressure of a livingsubject. As shown by FIG. 2B, the arterial pressure of living subject230 is detected via minimally invasive hemodynamic sensor 240 b. It isnoted that the features shown in FIG. 2B and identified by referencenumbers identical to those shown in FIG. 2A correspond respectively tothose previously described features, and may share any of thecharacteristics attributed to them above. It is further noted thathemodynamic sensor 240 b corresponds in general to hemodynamic sensor140, in FIG. 1, and may share any of the characteristics attributed tothat corresponding feature in the present application.

According to the implementation shown in FIG. 2B, hemodynamic sensor 240b is designed to sense an arterial pressure of living subject 230 in aminimally invasive manner. For example, hemodynamic sensor 240 b may beattached to living subject 230 via a radial arterial catheter insertedinto an arm of living subject 230. Alternatively, and although notexplicitly represented in FIG. 2B, in another implementation,hemodynamic sensor 240 b may be attached to living subject 230 via afemoral arterial catheter inserted into a leg of living subject 230.Like non-invasive hemodynamic sensing cuff 240 a, in FIG. 2A, minimallyinvasive hemodynamic sensor 240 b, in FIG. 2B, may be configured toprovide substantially continuous beat-to-beat monitoring of the arterialpressure of living subject 230 over an extended period of time, such asminutes or hours, for example.

Moving now to FIG. 3, FIG. 3 shows an exemplary system and acomputer-readable non-transitory medium including instructions enablingperformance of predictive weighting of hypotension profiling parameters.System 300, in FIG. 3, includes hardware unit 302 including systemprocessor 304, system memory 306, and display 326. Display 326 may takethe form of a liquid crystal display (LCD), a light-emitting diode (LED)display, an organic light-emitting diode (OLED) display, or anothersuitable display screen that performs a physical transformation ofsignals to light. System 300 including hardware unit 302 having systemprocessor 304 and system memory 306 corresponds in general to healthmonitoring system 100 including hardware unit 102 having systemprocessor 104 and system memory 106, and may share any of thecharacteristics attributed to that corresponding feature in the presentapplication. That is to say, system 302 may be configured to provideuser interface 120 and/or sensory alarm 128 using display 326.

Also shown in FIG. 3 is computer-readable non-transitory medium 318having hypotension prediction software code 310 including predictiveweighting module 316 stored thereon. The expression “computer-readablenon-transitory medium,” as used in the present application, refers toany medium, excluding a carrier wave or other transitory signal, thatprovides instructions to system processor 304 of hardware unit 302.Thus, a computer-readable non-transitory medium may correspond tovarious types of media, such as volatile media and non-volatile media,for example. Volatile media may include dynamic memory, such as dynamicrandom access memory (dynamic RAM), while non-volatile memory mayinclude optical, magnetic, or electrostatic storage devices. Commonforms of computer-readable non-transitory media include, for example,optical discs, RAM, programmable read-only memory (PROM), erasable PROM(EPROM), and FLASH memory.

According to the implementation shown in FIG. 3, computer-readablenon-transitory medium 318 provides hypotension prediction software code310 including predictive weighting module 316 for execution by systemprocessor 304. Hypotension prediction software code 310, when executedby system processor 304, instantiates a hypotension prediction softwarecode corresponding to hypotension prediction software code 110/210including predictive weighting module 116/216, in FIG. 1/2, and capableof performing all of the operations performed by that correspondingfeature and described in the present application.

Example implementations of the present inventive concepts will befurther described below with reference to FIG. 4 and FIG. 5. FIG. 4presents flowchart 460 outlining an exemplary method for use by a healthmonitoring system to perform predictive weighting of hypotensionprofiling parameters, while FIG. 5 shows a trace of an arterial pressurewaveform including exemplary indicia corresponding to the probability offuture hypotension in a living subject. The method outlined in flowchart460 can be performed using hypotension prediction software code110/210/310 including predictive weighting module 116/216/316 describedabove.

Flowchart 460 begins with obtaining by hypotension prediction softwarecode 110/210/310 executed by system processor 104/304, digitalhemodynamic data 144/244 converted, by ADC 122/222, from signal 142/242received from hemodynamic sensor 140/240 a/240 b (action 461). In oneimplementation, for example, hemodynamic sensor 140/240 a/240 b may beused to sense an arterial pressure of living subject 130/230 at anextremity of living subject 130/230, and to determine a central arterialpressure of living subject 130/230 based on the sensed arterialpressure.

Hardware unit 102/202/302 of system 100/200A/200B/300 may be configuredto receive the determined central arterial pressure of living subject130/230 as signal 142/242, which may be received as analog signals. Insuch an implementation, ADC 122/222 is used to convert signal 142/242into digital hemodynamic data 144/244. It is noted that in otherimplementations, hardware unit 102/202/302 can be configured todetermine the central arterial pressure of living subject 130/230 basedon a peripheral or brachial arterial pressure, for example, sensed byhemodynamic sensor 140/240 a/240 b and received by hardware unit102/202/302 as signal 142/242.

Referring to diagram 500, in FIG. 5, digital hemodynamic data 144/244may include various indicia predictive of a future hypotension event forliving subject 130/230 and extracted from arterial pressure waveform580, which may be a central arterial pressure waveform of living subject130/230. Diagram 500 shows exemplary indicia 582, 584, 586, and 588,corresponding respectively to the start of a heartbeat, the maximumsystolic pressure marking the end of systolic rise, the presence of thedicrotic notch marking the end of systolic decay, and the diastole ofthe heartbeat of living subject 130/230. Also shown by diagram 500 isexemplary slope 590 of arterial pressure waveform 580. It is noted thatslope 590 is merely representative of multiple slopes that may bemeasured at multiple locations along arterial pressure waveform 580.

In addition to the indicia 582, 584, 586, and 588 of arterial pressurewaveform 580 per se, the behavior of arterial pressure waveform 580during the intervals between those indicia may also be used as indiciapredictive of future hypotension for living subject 130/240. Forexample, the interval between the start of the heartbeat at indicia 582and the maximum systolic pressure at indicia 584 marks the duration ofthe systolic rise (hereinafter “systolic rise 582-584”). The systolicdecay of arterial pressure waveform 580 is marked by the intervalbetween the maximum systolic pressure at indicia 584 and the dicroticnotch at indicia 586 (hereinafter “systolic decay 584-586”). Together,systolic rise 582-584 and systolic decay 584-586 mark the entiresystolic phase (hereinafter “systolic phase 582-586”), while theinterval between the dicrotic notch at indicia 586 and the diastole atindicia 588 mark the diastolic phase of arterial pressure waveform 580(hereinafter “diastolic phase 586-588”).

Also of potential diagnostic interest is the behavior of arterialpressure waveform 580 in the interval from the maximum systolic pressureat indicia 584 to the diastole at indicia 588 (hereinafter “interval584-588”), as well as the behavior of arterial pressure waveform 580from the start of the heartbeat at indicia 582 to the diastole atindicia 588 (hereinafter “heartbeat interval 582-588”). The behavior ofarterial pressure waveform 580 during intervals: 1) systolic rise582-584, 2) systolic decay 584-586, 3) systolic phase 582-586, 4)diastolic phase 586-588, 5) interval 584-588, and 6) heartbeat interval582-588 may be determined by measuring the area under the curve ofarterial pressure waveform 580 and the standard deviation of arterialpressure waveform 580 in each of those intervals, for example. Therespective areas and standard deviations measured for intervals 1, 2, 3,4, 5, and 6 above (hereinafter “intervals 1-6”) may serve as additionalindicia predictive of future hypotension for living subject 130/240.

Flowchart 460 continues with obtaining, by hypotension predictionsoftware code 110/210/310 executed by system processor 104/304, one ormore vital sign parameters characterizing vital sign data, i.e.,characterizing vital sign data of living subject 130/230, from digitalhemodynamic data 144/244 (action 462). As noted above, digitalhemodynamic data 144/244 may include any or all of indicia 582, 584,586, 588, and 590. In addition, digital hemodynamic data 144/244 mayfurther include the respective areas and standard deviations measuredfor intervals 1-6 of arterial pressure waveform 580, as discussed above.According to the exemplary implementations shown in FIGS. 1, 2A, and 2B,digital hemodynamic data 144/244 is received by hypotension predictionsoftware code 110/210/310 from ADC 122/222.

The one or more vital sign parameters characterizing vital sign data mayinclude stroke volume, heart rate, respiration, and cardiaccontractility, to cite a few examples. Moreover, the one or more vitalsign parameters characterizing vital sign data may include a variety ofdifferent types of parameters found to be predictive of futurehypotension. For instance, the one or more vital sign parameterscharacterizing vital sign data may include any or all of mean arterialpressure (MAP), baroreflex sensitivity measures, hemodynamic complexitymeasures, and frequency domain hemodynamic features.

Baroreflex sensitivity measures quantify the relationship betweencomplementary physiological processes. For example, a decrease in bloodpressure in a healthy living subject is typically compensated by anincrease in heart rate and/or an increase in peripheral resistance. Thebaroreflex sensitivity measures that may be included in the one or morevital sign parameters characterizing vital sign data correspond to thedegree to which living subject 130/230 is responding appropriately tonormal physiological variations.

Hemodynamic complexity measures quantify the amount of regularity incardiac measurements over time, as well as the entropy, i.e., theunpredictability of fluctuations in cardiac measurements over time. Forexample, the present inventors have realized that unpredictable cardiacfluctuations are a normal phenomenon associated with health. Perhapscounterintuitively, very low entropy, in other words a high degree ofregularity in cardiac measurements over time and the substantial absenceof unpredictable fluctuations, can be a significant warning sign of animpending hypotension event. Frequency domain hemodynamic featuresquantify various measures of cardiac performance as a function offrequency rather than time.

Flowchart 460 continues with deriving differential parameters based onthe one or more vital sign parameters characterizing vital sign data(action 463). Obtaining differential parameters based on the one or morevital sign parameters (hereinafter “the differential parameters”) may beperformed by hypotension prediction software code 110/210/310 executedby system processor 104/304. The differential parameters may be derivedby determining the variations of one or more vital sign parameters withrespect to time, with respect to frequency, or with respect to otherparameters from among one or more vital sign parameters, for example. Asa result, each of one or more vital sign parameters may give rise toone, two, or several differential parameters included among hypotensionprofiling parameters 112/212.

For example, the differential parameter stroke volume variation (SVV)may be derived based on changes in the parameter stroke volume (SV) as afunction of time and/or as a function of sampling frequency.Analogously, changes in mean arterial pressure (AMAP) can be derived asa differential parameter with respect to time and/or sampling frequency,and so forth. As a further example, changes in mean arterial pressurewith respect to time can be derived by subtracting the average of themean arterial pressure over the past 5 minutes, over the past 10minutes, and so on from the current value of the mean arterial pressure.

Flowchart 460 continues with generating combinatorial parameters usingthe one or more vital sign parameters and the differential parameters(action 464). Generation of such combinatorial parameters (hereinafter“the combinatorial parameters”) may be performed by hypotensionprediction software code 110/210/310 executed by system processor104/304. For example, the combinatorial parameters may be generatedusing the one or more vital sign parameters and the differentialparameters by generating a power combination of a subset of the one ormore vital sign parameters and the differential parameters. It is notedthat, as used in the present application, the characterization “a subsetof the one or more vital sign parameters and the differentialparameters” refers to a subset that includes at least one of the one ormore vital sign parameters and/or at least one of the differentialparameters.

As a specific example, each of the combinatorial parameters may begenerated as a power combination of three parameters, which may berandomly or purposefully selected, from among the one or more vital signparameters characterizing vital sign data and/or the differentialparameters. Each of those three parameters selected from among the oneor more vital sign parameters and/or the differential parameters can beraised to an exponential power and can be multiplied with, or added to,the other two parameters analogously raised to an exponential power. Theexponential power to which each of the three parameters selected fromthe one or more vital sign parameters and/or the differential parametersis raised may be, but need not be, the same.

In some implementations, for example, generation of the combinatorialparameters may be performed using a predetermined and limited integerrange of exponential powers. For instance, in one such implementation,the exponential powers used to generate the combinatorial parameters maybe integer powers selected from among negative two, negative one, zero,one, and two (−2, −1, 0, 1, 2). Thus, each combinatorial parameter maytake the form:

X=Y ₁ ^(a) *Y ₂ ^(b) * . . . Y _(n) ^(e)  (Equation 1)

where each Y is one of one or more vital sign parameters characterizingvital sign data or one of the differential parameters, n is any integergreater than two, and each of a, b, and c may be any one of −2, −1, 0,1, and 2, for example. In one implementation, Equation 1 may be appliedto substantially all possible power combinations of the one or morevital sign parameters, the differential parameters, and the one or morevital sign parameters with the differential parameters, subject to thepredetermined constraints discussed above, such as the value of n andthe numerical range from which the exponential powers may be selected.

Hypotension profiling parameters 112/212 include one or more vital signparameters characterizing vital sign data, the differential parametersand the combinatorial parameters. Thus, actions 462, 463, and 464 resultin transformation of digital hemodynamic data 144/244 to hypotensionprofiling parameters 112/212. That is to say, digital hemodynamic data144/244 is transformed to hypotension profiling parameters 112/212 usinghypotension prediction software code 110/210/310, executed by systemprocessor 104/304, by identifying one or more vital sign parameterscharacterizing vital sign data, based on digital hemodynamic data144/244, obtaining the differential parameters based on one or morevital sign parameters, and generating the combinatorial parameters usingone or more of vital sign parameters and the differential parameters. Anexemplary but non-exhaustive table of hypotension profiling parameters112/212, as well as exemplary sampling criteria associated with theirdetermination, is provided as Appendix A of the present application.

Flowchart 460 continues with determining, by hypotension predictionsoftware code 110/210/310 using predictive weighting module 116/216/316and executed by system processor 104/304, a risk score corresponding tothe probability of a future hypotension event for living subject 130/230based on a weighted combination of hypotension profiling parameters112/212 (action 465). In other words, the risk score corresponding tothe probability of a future hypotension event for living subject 130/230is determined based on the one or more vital sign parameterscharacterizing vital sign data, the differential parameters and thecombinatorial parameters.

It is noted that in implementations in which one or more vital signparameters characterizing vital sign data includes the MAP of livingsubject 130/230, the weighting applied to the MAP may depend on thevalue of the MAP itself. Where the MAP is very high, the MAP may be arelatively unreliable predictor of hypotension and may consequently bevery lightly weighted. That is to say, where the MAP exceeds apredetermined upper limit threshold, for example, the weighting appliedto the MAP may be such that the weighted combination of hypotensionprofiling parameters 112/212 results in the MAP being substantiallydisregarded in determination of the risk score. By contrast, in othercases, the MAP may dominate the determination of the risk score.

It is emphasized that the risk score of living subject 130/230 isdetermined based on a weighted combination of hypotension profilingparameters 112/212, which in turn are derived from signal 142/242 ofliving subject 130/230 received from hemodynamic sensor 140/240 a/240 b.Consequently, according to the inventive concepts disclosed by thepresent application, system processor 104/304 of health monitoringsystem 100/200A/200B/300 is configured to execute hypotension predictionsoftware code 110/210/310 to determine the risk score of living subject130/230 without comparison with data corresponding to hypotension inother living subjects. In other words, hypotension prediction softwarecode 110/210/310 determines the risk score of living subject 130/230based on quantities derived from digital hemodynamic data 144/244, i.e.,hypotension profiling parameters 112/212, without reference to ahypotension patient database storing information regarding hypotensionin patients other than living subject 130/230.

By way merely of example, the risk score may be expressed as:

Risk Score=1/(1+e ^(−A))  (Equation 2)

Where:

A = c₀ + c₁ × v₁ + c₂ × v₂ + c₃ × v₃ + c₄ × v₄ + c₅ × v₅ + c₆ × v₆ + c₇ × v₇² × v₈² × v₉⁻² + c₈ × v₂² × v₁₀ × v₁₁⁻¹ + c₉ × Δ(v₁₂² × v₁₃² × v₁₄) + c₁₀ × Δ(v₁₅² × v₁ × v₁₆⁻¹) + c₁₁ × Δ(v₁₇² × v₁₈² × v₁₉⁻²)

And where:

-   -   v₁=CWI, the cardiac work indexed by patient's body surface area;    -   v₂=MAPavg, the averaged mean arterial pressure;    -   v₃=ΔMAPavg, the change of averaged mean arterial pressure when        compared to initial values;    -   v₄=avgSysDec, the averaged pressure at the decay portion of the        systolic phase;    -   v₅=ΔSys, the change of systolic pressure when compared to        initial values;    -   v₆=ppAreaNor, the normalized area under the arterial pressure        waveform;    -   v₇=biasDia, the bias of the diastolic slope;    -   v₈=CW, the cardiac work;    -   v₉=mapDnlocArea, the area under the arterial pressure waveform,        between first instance of MAP and the dicrotic notch;    -   v₁₀=SWcomb, the stroke work;    -   v₁₁=ppArea, the area under the arterial pressure waveform;    -   v₁₂=decAreallor, the normalized area of the decay phase;    -   v₁₃=slopeSys, the slope of the systolic phase;    -   v₁₄=Cwk, the Windkessel compliance;    -   v₁₅=sys_rise_area_nor, the normalized area under the systolic        rise phase;    -   v₁₆=pulsepres, the pulse pressure;    -   v₁₇=avg_sys, the averaged pressure of the systolic phase;    -   v₁₈=dpdt2, the maximum value of the second order derivative of        the pressure waveform;    -   v₁₉=dpdt, the maximum value of the first order derivative of the        pressure waveform; and    -   Δ=the change of the value when compared to its initial value    -   c₀, c₁, . . . , c₁₁ are constant coefficients.

In some implementations, the risk score may be expressed as a fraction,as represented by Equation 2. However, in other implementations, therisk score may be converted to a percentage risk score between zeropercent and one hundred percent.

Flowchart 460 can conclude with invoking, by hypotension predictionsoftware code 110/210/310 executed by system processor 104/304, sensoryalarm 128 if the risk score satisfies a predetermined risk criterion(action 466). As shown in FIG. 1, for example, hypotension predictionsoftware code 110/210/310 may be configured to provide an output to userinterface 120 on display 126/326 for displaying the risk score, and/orfor invoking sensory alarm 128. As further shown in FIG. 1, in someimplementations, the output of hypotension prediction software code110/210/310 may be processed using DAC 124 to convert digital signalsinto analog signals for presentation via user interface 120.

The predetermined risk criterion may be based on the value of the riskscore, on the trend of the risk score over a time interval, or both. Forexample, where the risk score is expressed as a percentage between zeroand one hundred, having the risk score exceed a threshold of eighty-fivepercent, for instance, may cause sensory alarm 128 to be invokedimmediately. Alternatively, or in addition, a lower risk score may causesensory alarm 128 to be invoked if it exceeds a predetermined thresholdover the entirety of a predetermined time period.

Thus, for example, while a risk score of eighty five percent may causesensory alarm 128 to be invoked immediately, a risk score of eightypercent may cause sensory alarm 128 to be invoked after several secondsat that level, such as ten to thirty seconds in which the risk score iscontinuously between eighty and eighty five percent, for example. Byanalogy, a still lower risk score may cause sensory alarm 128 to beinvoked if that risk score is maintained continuously for one or moreminutes. In yet another implementation, the risk score may cause sensoryalarm 128 to be invoked if it meets or exceeds a predetermined value apredetermined number of times over a predetermined time period. Forexample, having the risk score exceed seventy five percent three timesover a five minute interval may cause sensory alarm 128 to be invoked.

As noted above by reference to FIG. 1, sensory alarm 128 may beimplemented as one or more of a visual alarm, an audible alarm, and ahaptic alarm. For example, when implemented to provide a visual alarm,sensory alarm 128 may be invoked as flashing and/or colored graphicsshown by user interface 120 on display 126, and/or may includedisplaying the risk score via user interface 120 on display 126/326.When implemented to provide an audible alarm, sensory alarm 128 may beinvoked as any suitable warning sound, such as a siren or repeated tone.Moreover, when implemented to provide a haptic alarm, sensory alarm 128may cause hardware unit 102/302 to vibrate or otherwise deliver aphysical impulse perceptible to user 150.

Although not included among the actions outlined by flowchart 460, insome implementations, the present method may include identifying, byhypotension prediction software code 110/210/310 executed by systemprocessor 104/304, a most probable cause of the future hypotension eventof living subject 130/230. For example, and as noted above, based onindicia included in digital hemodynamic data 144/244, hypotensionprediction software code 110/210/310 may be used to identify poorvascular tone, low blood volume, or reduced cardiac contractility, toname a few exemplary causes, as a most probable cause of a predictedfuture hypotension event.

In addition, in some implementations, the present method may includerecommending, by hypotension prediction software code 110/210/310executed by system processor 104/304, a medical intervention forpreventing the future hypotension event of living subject 130/230. Withrespect to poor vascular tone or low blood volume, for example,administration of a vasoconstrictor may be recommended if poor vasculartone is detected, while administration of saline or whole blood may berecommend if low blood volume is identified as a most probable cause ofthe predicted future hypotension event.

Thus, by converting data received from a hemodynamic sensor to digitalhemodynamic data of a living subject, and by transforming the digitalhemodynamic data to multiple hypotension profiling parameters, thepresent solution employs a powerful multivariate model for predictingfuture hypotension. The solution disclosed by the present applicationthen determines a risk score corresponding to the probability of afuture hypotension event for the living subject based on a weighedcombination of the multiple hypotension profiling parameters. Inaddition, by invoking a sensory alarm if the risk score satisfies apredetermined risk criterion, the present application discloses asolution that provides an early warning of a future hypotension eventfor the living subject, thereby advantageously enabling health careworkers to prepare a timely and effective intervention.

From the above description it is manifest that various techniques can beused for implementing the concepts described in the present applicationwithout departing from the scope of those concepts. Moreover, while theconcepts have been described with specific reference to certainimplementations, a person of ordinary skill in the art would recognizethat changes can be made in form and detail without departing from thescope of those concepts. As such, the described implementations are tobe considered in all respects as illustrative and not restrictive. Itshould also be understood that the present application is not limited tothe particular implementations described herein, but manyrearrangements, modifications, and substitutions are possible withoutdeparting from the scope of the present disclosure.

APPENDIX A Hypotension Profiling Parameter Description SamplingCriteria 1. TR_bp_dia: Diastolic pressure 20 sec. average 2. TR_c_wk:The Windkessel Compliance 20 sec. average (based on the Langewooterspaper) 3. TR_CO_disp: Cardiac output 20 sec. average 4. TR_CO_hsi:Cardiac output computed 20 sec. average with a heavily weightedmultivariate model based on hyperdynamic conditions 5. TR_COaccum_avg:Cardiac output - 5 min.  5 min. average average 6. TR_dia_area_nodia:Area under the 20 sec. average arterial pressure waveform from thedicrotic notch to the start of the next beat with subtracted diastolicpressure 7. TR_dpdt_var: Variability in maximum of the 20 sec. averagefirst derivative 8. TR_dpdt2_var: Variability in maximum of the 20 sec.average second derivative 9. TR_HR_avg_disp: Heart rate - 5 min. average 5 min. average 10. TR_K_avg_fp_tp: Multivariate classification 20 sec.average model to detect the likelihood of a false positive in theprediction of K_avg_dm 11. TR_t_sys_rise_var: Variability in 20 sec.average TR_t_sys_rise 12. TR_K_avg_hyp_w: Vascular tone computed 20 sec.average from a weighted multivariate model derived from severehyperdynamic conditions 13. TR_K_avg_lco: Multivariate classification 20sec. average model to detect low flow conditions 14. TR_kmult: Arterialtone estimate 20 sec. average 15. TR_kmult_fp_tp: K_avg_fp_tp - 20 sec.20 sec. average Average 16. TR_kurt: The kurtosis of the arterial 20sec. average pressure waveform within a beat 17. TR_kurt_var:Variability in the 20 sec. average kurtosis of the arterial pressurewaveform within a beat 18. TR_sku: Skewness of the arterial 20 sec.average pressure waveform within a beat 19. TR_sku_var: Variability inTR_sku 20 sec. average 20. TR_sku2: Skewness of the 20 sec. 20 sec.average reconstructed arterial pressure waveform 21. TR_slope_dia:Diastolic slope 20 sec. average 22. TR_slope_dia_var: Variability in the20 sec. average diastolic slope 23. TR_slope_sys: Slope of the systolicrise 20 sec. average 24. TR_SVV_avg_disp: Stroke volume  5 min. averagevariation (SVV) - 5 min. average 25. TR_SVV_disp: SVV sensed byhemodynamic 20 sec. average sensor 140/240a/240b 26. TR_SVV_resp: SVVcomputed with the 20 sec. average detection of the respiratory cycles inthe signal 27. TR_t_dec_var: Variability in time 20 sec. average fromsystolic maximum to start of next heart beat 28. TR_t_sys: Duration ofthe systolic 20 sec. average phase from the start of the heart beat tothe dicrotic notch 29. TR_t_sys_dec: Time from the systolic 20 sec.average maximum to the dicrotic notch 30. TR_t_sys_rise: Time from thestart of 20 sec. average the heart beat to the systolic maximum

What is claimed is:
 1. A system for monitoring of arterial pressure of apatient and providing a warning to medical personnel of a predictedfuture hypotensive event, the system comprising: a hemodynamic sensorthat produces an analog hemodynamic sensor signal representative of anarterial pressure waveform of the patient; an analog-to-digitalconverter that converts the analog hemodynamic sensor signal to digitalhemodynamic data; a system memory that stores hypotension predictionsoftware code including a predictive weighting module; a user interfacethat includes a sensory alarm that provides a sensory signal to warnmedical personnel of the predicted future hypotensive event prior to thepatient entering a hypotensive state; and a hardware processor that isconfigured to execute the hypotension prediction software code to:perform waveform analysis of the digital hemodynamic data to obtainvital sign parameters from the digital hemodynamic data; derivedifferential parameters based on one or more of the vital signparameters; generate combinatorial parameters using one or more of thevital sign parameters and/or one or more of the differential parameters;determine, using the predictive weighting module, a risk scorerepresenting a probability of a future hypotension event for the patientbased on a weighted combination of a plurality of hypotension profilingparameters including the one or more of the vital sign parameters, thedifferential parameters and the combinatorial parameters; and invoke thesensory alarm to produce the sensory signal in response to the riskscore satisfying a predetermined risk criterion.
 2. The system of claim1, wherein the hardware processor transforms the digital hemodynamicdata to obtain the vital sign parameters by executing the hypotensionprediction software code to: determine, from the digital hemodynamicdata, on a heartbeat-by-heartbeat basis, indicia representative of oneor more of: start of a heartbeat; maximum systolic pressure marking endof systolic rise; presence of a dicrotic notch marking end of systolicdecay; diastole of the heartbeat; and slopes of an arterial pressurewaveform; determine, based on the indicia, one or more intervals fromthe group consisting of: systolic rise interval; systolic decayinterval; systolic phase interval; diastolic phase interval; maximumsystolic pressure to diastole interval; and heartbeat interval; andproduce one or more parameters representing behavior of the arterialpressure waveform during the one or more intervals.
 3. The system ofclaim 1, wherein the plurality of hypotension profiling parameterscomprise at least one of: stroke volume; heart rate; respiration rate;cardiac contractability; mean arterial pressure (MAP); baroreflexsensitivity measures; hemodynamic complexity measures; and frequencydomain hemodynamic features.
 4. The system of claim 1, wherein thehardware processor is further configured to execute the hypotensionprediction software code to identify a most probable cause of the futurehypotension event.
 5. The system of claim 4, wherein the hardwareprocessor is further configured to execute the hypotension predictionsoftware code to recommend a medical intervention for preventing thefuture hypotension event.
 6. The system of claim 1, wherein thehemodynamic sensor is a noninvasive hemodynamic sensor that isattachable to an extremity of the patient.
 7. The system of claim 1,wherein the hemodynamic sensor is a minimally invasive arterial catheterbased hemodynamic sensor.
 8. The system of claim 1, wherein the sensoryalarm comprises a visual alarm.
 9. The system of claim 1, wherein thesensory alarm comprises an audible alarm.
 10. The system of claim 1,wherein the sensory alarm comprises a haptic alarm.
 11. The system ofclaim 1, wherein the vital sign parameters include one or more of strokevolume, heart rate, respiration, and cardiac contractibility.
 12. Thesystem of claim 11, wherein the differential parameters are derived bythe hardware processor to represent variations in one or more of thevital sign parameters with respect to time, with respect to frequency,or with respect to other vital sign parameters.
 13. The system of claim12, wherein the differential parameters include stroke volume variation(SVV) based upon changes in stroke volume as a function of time orsampling frequency.
 14. The system of claim 12, wherein the differentialparameters include change of mean arterial pressure (ΔMAP) as a functionof time or sampling frequency.
 15. The system of claim 1, wherein thecombinatorial parameters comprise a combination of vital signparameters, a combination of differential parameters, or a combinationof at least one vital sign parameter and at least one differentialparameter.
 16. The system of claim 15, wherein the combinatorialparameters comprise a multiplication or addition of vital signparameters, a multiplication or addition of combinational parameters, ora multiplication or addition of both a vital sign parameter and acombinational parameter.
 17. The system of claim 15, wherein thecombinatorial parameters comprise a power combination in which eachvital sign parameter and differential parameter has a correspondingexponential power.
 18. The system of claim 1, wherein the hardwareprocessor is configured to invoke the sensory alarm immediately when therisk score satisfies a first risk criterion.
 19. The system of claim 18,wherein the hardware processor is configured to invoke the sensory alarmwhen the risk score satisfies a second risk criterion continuously for afirst predetermined time period.
 20. The system of claim 19, wherein thehardware processor is configured to invoke the sensory alarm when therisk score satisfies a third risk criterion continuously for a second,longer, predetermined time period.
 21. The system of claim 18, whereinthe hardware processor is configured to invoke the sensory alarm whenthe risk score satisfies a fourth risk criterion a predetermined numberof times over a predetermined time interval.
 22. The system of claim 1wherein the risk score is determined by a formula:Risk Score=1/(1+e ^(−A)).
 23. The system of claim 22, where:A=c ₀ +c ₁ ×v ₁ +c ₂ ×v ₂ +c ₃ ×v ₃ +c ₄ ×v ₄ +c ₅ ×v ₅ +c ₆ ×v ₆ +c ₇×v ₇ ² ×v ₈ ² ×v ₉ ⁻² +c ₈ ×v ₂ ² ×v ₁₀ ×v ₁₁ ⁻¹ +c ₉×Δ(v ₁₂ ² ×v ₁₃ ²×v ₁₄)+c ₁₀×Δ(v ₁₅ ² ×v ₁ ×v ₁₆ ⁻¹)+c ₁₁×Δ(c ₁₇ ² ×v ₁₈ ² ×v ₁₉ ⁻²) 24.The system of claim 23, where: v₁=cardiac work indexed by patient's bodysurface area; v₂=averaged mean arterial pressure; v₃=change of averagedmean arterial pressure when compared to initial values; v₄=averagedpressure at a decay portion of the systolic phase; v₅=change of systolicpressure when compared to initial values; v₆=normalized area under anarterial pressure waveform; v₇=bias of diastolic slope; v₈=cardiac work;v₉=area under the arterial pressure waveform, between first instance ofMAP and the dicrotic notch; v₁₀=stroke work; v₁₁=area under the arterialpressure waveform; v₁₂=normalized area of decay phase; v₁₃=slope ofsystolic phase; v₁₄=Windkessel compliance; v₁₅=normalized area undersystolic rise phase; v₁₆=pulse pressure; v₁₇=averaged pressure ofsystolic phase; v₁₈=maximum value of a second order derivative of thearterial pressure waveform; v₁₉=maximum value of a first orderderivative of the arterial pressure waveform; Δ=change of a value whencompared to its initial value; and c₀, c₁, . . . , c₁₁ are constantcoefficients.
 25. The system of claim 22, wherein the hardware processorconverts the risk score to a percentage risk score between zero percentand one hundred percent.